paper1_figures

library(latex2exp)
library(tidyverse)
library(ggpubr)

#load the data ###AIC values of the 4 scenarios along with the parameters estimated

aic.df <- read_csv("~/plots/all_data/aic.csv")

###add the replicate info

aic.df <- aic.df %>% 
  mutate(exp.field = paste0(str_split(cell.id, "_", simplify = T)[,2],"_",
                            str_split(cell.id, "_", simplify = T)[,3]),
    colony = case_when(exp.field %in% c("20min_s3", "20min_s4") ~ "Replicate 1", 
                            exp.field %in% c("20min_s5", "20min_s6") ~ "Replicate 2",
                            exp.field %in% c("20min_s7", "20min_s8" , "20min_s9") ~ "Replicate 3"))

aic.df <- aic.df %>% 
  mutate(colony = ifelse(degron == "stable" & red == "pup1-rfp", 
                         case_when(exp.field %in% c("20min_s4", "20min_s5") ~ "Replicate 1",
                                   exp.field %in% c("20min_s6", "20min_s7") ~ "Replicate 2",
                                   exp.field %in% c("20min_s8", "20min_s9") ~ "Replicate 3"), colony)) 
  # filter(value < 0.05)
  

###Cellular attributes

pup1.cell.attr <- read_csv("~/plots/all_data/all_pup1_cell_attr.csv")

#pup1-RFP background

pup1.cell.attr <- pup1.cell.attr %>% 
  mutate(exp.field = paste0(str_split(cell.id, "_", simplify = T)[,2],"_",
                            str_split(cell.id, "_", simplify = T)[,3]),
    colony = case_when(exp.field %in% c("20min_s3", "20min_s4") ~ "Replicate 1", 
                            exp.field %in% c("20min_s5", "20min_s6") ~ "Replicate 2",
                            exp.field %in% c("20min_s7", "20min_s8" , "20min_s9") ~ "Replicate 3"))

pup1.cell.attr <- pup1.cell.attr %>% 
  mutate(colony = ifelse(degron == "stable" & red == "pup1-rfp", 
                         case_when(exp.field %in% c("20min_s4", "20min_s5") ~ "Replicate 1",
                                   exp.field %in% c("20min_s6", "20min_s7") ~ "Replicate 2",
                                   exp.field %in% c("20min_s8", "20min_s9") ~ "Replicate 3"), colony)) 
 
multiple.pup1 <- pup1.cell.attr %>% 
  mutate(dapi.mean.bg.sub.puncta = ifelse(is.na(dapi.mean.bg.sub.puncta), 0, dapi.mean.bg.sub.puncta) , 
         rfp.mean.bg.sub.puncta = ifelse(is.na(rfp.mean.bg.sub.puncta), 0 , rfp.mean.bg.sub.puncta)) %>% 
  filter(rfp.mean.bg.sub.puncta > 0) %>% group_by(cell.id) %>% tally() %>% filter(n>1)

pup1.cell.attr <-  pup1.cell.attr %>% 
  group_by(cell.id) %>% 
  mutate(rfp.mean.bg.sub.puncta = ifelse(cell.id %in% multiple.pup1$cell.id , mean(rfp.mean.bg.sub.puncta), rfp.mean.bg.sub.puncta )) %>% 
    distinct(cell.id, .keep_all = TRUE) 

###Number of data points for each cell

no.of.dp <- read_csv("~/plots/all_data/all_exp_data.csv") %>% 
  filter(cell.id %in% figS2.df$cell.id) %>% 
  group_by(cell.id) %>% 
tally()

no.of.dp %>% filter(n > 31)

#dy estimated using the 1-parameter exponential and 2-parameter with maturation model We need both the models because we are comparing the rate of decay from both the models

dy.pup1 <- aic.df %>% 
  filter(red == "pup1-rfp") %>% 
  filter(ifelse(degron %in% c("stable.2","stable","stabe.3"), dy < 0.1, dy < 0.5)) %>% 
  group_by(cell.id) %>% 
  mutate(dm = ifelse(is.na(dm), Inf, dm)) %>%
  filter( dm > 0.00001) %>% 
  filter(model %in% c("dy.dm","exponential"))
  

#number of cells that converged in both the models

dy.pup1 %>% 
  filter(degron %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3")) %>% 
  select(cell.id, model, dy, aic,degron) %>% 
  pivot_wider(values_from = c("dy","aic"), names_from = model) %>% 
  na.omit() %>% 
  group_by(degron) %>% 
  tally()

Error in select(., cell.id, model, dy, aic, degron) : 
  unused arguments (cell.id, model, dy, aic, degron)

Final df used to generate the figures

figS2.df <- dy.pup1 %>% 
  filter(treatment == "none") %>% 
  left_join(.,pup1.cell.attr %>%
              filter(red == "pup1-rfp", treatment == "none"),
            by = c("cell.id","treatment","degron","red","colony","exp.field")) %>% 
  mutate(dapi.mean.bg.sub.puncta = ifelse(is.na(dapi.mean.bg.sub.puncta), 0, dapi.mean.bg.sub.puncta) , 
         rfp.mean.bg.sub.puncta = ifelse(is.na(rfp.mean.bg.sub.puncta), 0 , rfp.mean.bg.sub.puncta)) %>% 
  filter(rfp.mean.bg.sub.puncta > 0) %>% 
  filter(ifelse(degron %in% c("stable.3","stable.2"), dy < 0.1, dy < 0.5))

Looking at how many cells converged in the mat model and in exponential model

#total no. of cells which converged in the the maturation + decay model but not in the exponential model
figS2.df %>% 
  dplyr::select(cell.id, dy, model, degron) %>% 
  dplyr::ungroup() %>% 
  pivot_wider(values_from = dy, names_from = model) %>% 
  filter(is.na(exponential)) %>% 
  group_by(degron) %>% 
  tally()

#total no. of cells which converged in the exponential model but not in the maturation + decay model
figS2.df %>% 
  dplyr::select(cell.id, dy, model, degron) %>% 
  dplyr::ungroup() %>% 
  pivot_wider(values_from = dy, names_from = model) %>% 
  filter(is.na(dy.dm)) %>% 
  group_by(degron) %>% 
  tally()

#percent of cells with lowest AIC scores in each model

#underestimation of decay rates by exponential model grouped by

figS2.df %>% 
  dplyr::select(cell.id, dm, dy, model, degron, gfp.mean.bg.af.sub.new) %>% 
  dplyr::ungroup() %>% 
  pivot_wider(values_from = c("dy","dm"), names_from = model) %>% 
  mutate(dm.level = ifelse(dm_dy.dm > 1, ">1", "<1"),
    exp.mat.ratio = ((dy_dy.dm - dy_exponential)/dy_dy.dm)*100) %>% 
  filter(!(is.na(exp.mat.ratio)))%>%
  filter(degron %in% c("mODC.2","cln2.3","stable.3","stable.2","cln2.4")) %>% 
  mutate(degron = case_when(degron %in% c("cln2.3","cln2.4") ~ "GFP-CLN2",
                            degron %in% c("mODC.2") ~ "GFP-mODC",
                            degron %in% c("stable.2","stable.3") ~ "GFP"),
         degron = factor(degron, levels = c("GFP-mODC","GFP-CLN2","GFP"))) %>%
  group_by(degron, dm.level) %>% 
  summarise(median(exp.mat.ratio) %>% signif(.,digits = 2), 
            sd(exp.mat.ratio),
            NROW(exp.mat.ratio))

`summarise()` has grouped output by 'degron'. You can override using the `.groups` argument.

figS2.df %>% 
  dplyr::select(cell.id, dy, model, degron, gfp.mean.bg.af.sub.new) %>% 
  dplyr::ungroup() %>% 
  pivot_wider(values_from = dy, names_from = model) %>% 
  mutate(exp.mat.ratio = (dy.dm - exponential)/dy.dm) %>% 
  filter(!(is.na(exp.mat.ratio))) %>%
  filter(degron %in% c("mODC.2","cln2.3","cln2.4","stable.3","stable.2")) %>% 
  mutate(degron = case_when(degron %in% c("cln2.3","cln2.4") ~ "GFP-CLN2", 
                            degron %in% c("mODC.2") ~ "GFP-mODC", 
                            degron %in% c("stable.2","stable.3") ~ "GFP"), 
         degron = factor(degron, levels = c("GFP-mODC","GFP-CLN2","GFP"))) %>% 
  ggplot(.,aes(x = gfp.mean.bg.af.sub.new, y = exp.mat.ratio))+
  geom_point(size = 0.2)+
  facet_wrap(~degron, scales = "free")+
  theme_pubr()

#bar plot to show which model best explains the decay

figS2.df %>% 
  filter(model %in% c("dy.dm","exponential")) %>% 
  group_by(cell.id) %>% 
  filter(aic == min(aic)) %>% 
  ungroup() %>% 
  # pivot_wider(names_from = name, values_from = value) %>% 
  group_by(treatment, red, degron, colony,model) %>% 
  tally() %>% 
  ungroup() %>% 
  filter(treatment == "none") %>% 
  ggplot(.,aes(x = model, y= n, fill = colony))+
  geom_col(position = "dodge")+
  facet_wrap(~degron, scales = "free")
  # theme(axis.text.x = element_text(angle = 45, hjust = 1))

aic.df %>% 
  filter(model %in% c("dy.dm","exponential")) %>% 
  group_by(cell.id) %>% 
  filter(aic == min(aic)) %>% 
  ungroup() %>% 
  # pivot_wider(names_from = name, values_from = value) %>% 
  group_by(treatment, red, degron, model) %>% 
  tally() %>% 
  ungroup() %>% 
  filter(treatment != "none") %>% 
  ggplot(.,aes(x = model, y= n))+
  geom_col()+
  facet_wrap(~treatment, scales = "free_y")+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

high.dm <- figS2.df %>% 
  filter(model %in% c("dy.dm")) %>% 
  filter( treatment == "none", red == "pup1-rfp") %>% 
  filter(dm > 1)

low.dm <- figS2.df %>% 
  filter(model %in% c("dy.dm")) %>% 
  filter(treatment == "none", red == "pup1-rfp") %>% 
  filter(dm < 1)

#modified supplemtal figures #the values of dy when

#cln2
figS2.df %>% 
  filter(model %in% c("dy.dm","exponential") ) %>% 
  filter(degron %in% c("cln2","cln2.2","cln2.3","cln2.4"), treatment == "none", red == "pup1-rfp") %>% 
  filter(dy < 0.5) %>% 
  mutate(dm.level = ifelse(cell.id %in% high.dm$cell.id , "High", "low")) %>% 
  select(cell.id, degron, dy, model, dm.level) %>% 
  pivot_wider(values_from = dy, names_from = model) %>% 
  ggplot(.,aes(x = exponential, y = dy.dm, color = dm.level))+
    geom_point(size = 0.5)+
  geom_abline(slope = 1, color = "firebrick1")+
  stat_cor()+
  theme_pubr()+
  xlab(TeX("Rate of Decay from a one parameter model (Eq. 8), $\\delta , min^{-1}$"))+
  ylab(TeX("Rate of Decay from a model with maturation (Eq. 6), $\\delta , min^{-1}$"))+
  theme(text = element_text(size = 8))

#stable
figS2.df %>% 
  filter(rfp.mean.bg.sub.puncta > 0) %>% 
  filter(model %in% c("dy.dm","exponential") ) %>% 
  filter(degron %in% c("stable","stable.2","stable.3"), treatment == "none", red == "pup1-rfp") %>% 
  filter(dy < 0.1) %>% 
  mutate(dm.level = ifelse(cell.id %in% high.dm$cell.id , "High", "low")) %>% 
  select(cell.id, degron, dy, model, dm.level) %>% 
  pivot_wider(values_from = dy, names_from = model) %>% 
  ggplot(.,aes(x = exponential, y = dy.dm, color = dm.level))+
    geom_point(size = 0.5)+
  geom_abline(slope = 1, color = "firebrick1")+
  stat_cor()+
  theme_pubr()+
  xlab(TeX("Rate of Decay from a one parameter model (Eq. 8), $\\delta , min^{-1}$"))+
  ylab(TeX("Rate of Decay from a model with maturation (Eq. 6), $\\delta , min^{-1}$"))+
  theme(text = element_text(size = 8))

  

#dm values histogram

dm.distPlt

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#percent of cells with dm values > 1 and < 1 in the maturation model

modc.dy.comparePlt

Warning: Removed 570 rows containing non-finite values (stat_cor).
Warning: Removed 570 rows containing missing values (geom_point).

figS2.df %>% 
  filter(model == "dy.dm", red == "pup1-rfp", treatment =="none", degron %in% c("mODC.2","cln2.3","cln2.4","stable.2","Stable.3")) %>% 
  left_join(.,no.of.dp, by = "cell.id") %>% 
  group_by(cell.id) %>%
  mutate(rmsd = sqrt(value/n)) %>%
  ungroup() %>% 
  group_by(degron, colony) %>% 
  mutate(rmsd.mean.norm = rmsd/mean(rmsd)) %>% 
  ggplot(.,aes(x = rmsd.mean.norm ))+
  geom_histogram()+
  facet_grid(colony~degron, scales = "free")

figS2.df %>% 
  filter(model == "dy.dm", red == "pup1-rfp", treatment =="none", degron %in% c("mODC.2","cln2.3","cln2.4","stable.2","Stable.3")) %>% 
  left_join(.,no.of.dp, by = "cell.id") %>% 
  group_by(cell.id) %>%
  mutate(rmsd = sqrt(value/n))%>%
  ungroup() %>% 
  group_by(degron, colony) %>% 
  mutate(rmsd.mean.norm = rmsd/mean(rmsd)) %>% 
  ggplot(.,aes(x = rmsd ))+
  geom_histogram()+
  facet_grid(colony~degron, scales = "free")

figS2.df %>% 
  filter(red == "pup1-rfp", treatment =="none", degron %in% c("mODC.2","cln2.3","cln2.4","stable.2","Stable.3")) %>% 
  left_join(.,no.of.dp, by = "cell.id") %>% 
  group_by(cell.id) %>%
  mutate(rmsd = sqrt(value/n))%>%
  ungroup() %>% 
  group_by(degron, model) %>% 
  mutate(rmsd.mean.norm = rmsd/mean(rmsd)) %>% 
  filter(rmsd < 0.) %>% 
  ggplot(.,aes(x = rmsd, color = model))+
  geom_freqpoly()+
  facet_wrap(~degron, scales = "free")+
  theme_pubr()+
  theme(text = element_text(size = 8))

#aic scores

aic.score.plt

Warning: Removed 923 rows containing missing values (geom_point).

aic.score.ptDenPlt

Warning: Removed 1492 rows containing non-finite values (stat_pointdensity).

#dy scatterplot

dy.models.scatPlt

Warning: Removed 1491 rows containing non-finite values (stat_pointdensity).

distributions of dy geoom_freak_poly

dy.models.den.plt

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Underestimation of the decay rates Decay rates are higher in the maturation model compared to the exponential model

df.underEst <- figS2.df %>% 
  dplyr::select(cell.id, dm, dy, model, degron, gfp.mean.bg.af.sub.new) %>% 
  dplyr::ungroup() %>% 
  pivot_wider(values_from = c("dy","dm"), names_from = model) %>% 
  mutate(dm.level = ifelse(dm_dy.dm > 1, ">1", "<1"),
    exp.mat.ratio = ((dy_dy.dm - dy_exponential)/dy_dy.dm)*100) %>% 
  filter(!(is.na(exp.mat.ratio)))%>%
  filter(degron %in% c("mODC.2","cln2.4","cln2.3","stable.2","stable.3")) %>% 
  mutate(degron = case_when(degron %in% c("cln2.3","cln2.4") ~ "yeGFP-CLN2",
                            degron %in% c("mODC.2") ~ "yeGFP-mODC",
                            degron %in% c("stable.2","stable.3") ~ "yeGFP"),
         degron = factor(degron, levels = c("yeGFP-mODC","yeGFP-CLN2","yeGFP"))) %>% 
  group_by(degron, dm.level) %>% 
  summarise(n = NROW(exp.mat.ratio), 
            exp.mat.ratio = min(exp.mat.ratio)-2)

`summarise()` has grouped output by 'degron'. You can override using the `.groups` argument.

Added the protein inhibiition data set

figs2.prtIn <- dy.pup1 %>% 
  left_join(.,pup1.proInhi.attr %>%
              filter(red == "pup1-rfp"),
            by = c("cell.id","treatment","degron","red","colony","exp.field")) %>% 
  mutate(dapi.mean.bg.sub.puncta = ifelse(is.na(dapi.mean.bg.sub.puncta), 0, dapi.mean.bg.sub.puncta) , 
         rfp.mean.bg.sub.puncta = ifelse(is.na(rfp.mean.bg.sub.puncta), 0 , rfp.mean.bg.sub.puncta)) %>% 
  filter(rfp.mean.bg.sub.puncta > 0)

#with proteasome inhibition ##percent cells with mu < 1

figs2.prtIn %>% 
  filter(rfp.mean.bg.sub.puncta > 0,
         treatment != "none") %>% 
  filter(model == "dy.dm") %>% 
  filter(ifelse(treatment == "5uM", dy > 0.005, dy < 0.5), 
         dm > 0.00001) %>% 
  mutate(dm.level = ifelse(dm > 1, "high", "low"),
    degron = case_when(degron %in% c("mODC","mODC.2") ~ "GFP-mODC",
                            degron %in% c("cln2.2","cln2","cln2.3","cln2.4") ~ "GFP-CLN2",
                            degron %in% c("stable","stable.2","stable.3") ~ "GFP"), 
         treatment = factor(treatment, levels = c("dmso1","dmso2","1uM","2.5uM","5uM","50uM"))) %>% 
  group_by(treatment, dm.level) %>% 
  tally() %>% 
  pivot_wider(values_from = n, names_from = dm.level) %>% 
  mutate(per.low = (low/(low+high))*100 ) %>% 
  ggplot(.,aes(x = treatment, y = per.low))+
  geom_col(width = 0.2)+
  theme_pubr()+
  ylab(bquote(atop("% of cells with" , "rate of maturation \u03bc < 1"~ min^{-1})))+
  # ylab(TeX("% of cells with rate of maturation $(\\mu) < 1\\ min^{-1}$"))+
  theme(text = element_text(size = 8), 
        legend.position = c(0.5, 1), 
        legend.direction = "horizontal", 
        legend.background = element_blank(), 
        legend.key.size = unit(2 ,"mm"),
         axis.line = element_line(size = 0.1),
        axis.ticks = element_line(size = 0.2),
        axis.title.x = element_blank())+
  scale_x_discrete(labels = function(x) str_wrap(x, width = 3))

log(2)/0.5000000

[1] 1.386294

##AIC scores

figs2.prtIn %>% 
  filter(rfp.mean.bg.sub.puncta > 0,
         treatment != "none") %>% 
filter(model %in% c("dy.dm","exponential")) %>%
  group_by(cell.id) %>% 
  filter(!(is.na(dy)), 
         ifelse(degron == "5uM", dy > 0.005000000, dy < 0.5)) %>% 
  dplyr::select(cell.id, treatment, dm,dy,aic, model, colony) %>% 
  ungroup() %>% 
  pivot_wider(values_from = c("dy","aic","dm"), names_from = "model")  %>% 
  mutate(dm_dy.dm = ifelse(is.na(dm_dy.dm), Inf, dm_dy.dm),
    dm.level = ifelse(dm_dy.dm > 1, "high", "low")) %>%
  mutate( treatment = case_when(treatment %in% c("dmso1","dmso2") ~ "DMSO",
                                TRUE ~ treatment), 
         treatment = factor(treatment, levels = c("DMSO","1uM","2.5uM","5uM","50uM"))) %>% 
  ggplot(.,aes(y = aic_exponential, x = aic_dy.dm))+
  # geom_hex(bins = 100)+
  ggpointdensity::geom_pointdensity(size = 0.2, adjust = 0.5)+
  geom_abline(slope = 1, color = "red4", alpha = 0.5)+
  facet_wrap(~treatment, scales = "free")+
  theme_pubr()+
  ylab(TeX("Exponential model ($\\delta_{exp}$)"))+
  xlab(TeX("2-parameter model w Maturation ($\\delta_{mat}$)"))+
  labs(title = "Rate of Decay")+
  xlim(-350,-100)+
  ylim(-350,-100)+
  theme(text = element_text(size = 12),#changed to 12 for GRC from 8
        axis.text.x = element_text(angle = 30), 
        axis.line = element_line(size = 0.1),
        axis.ticks = element_line(size = 0.2),
        strip.background = element_blank(),
        legend.key.size = unit(1.5,"mm"),
        legend.position = c(0.95,0.2),
        legend.background = element_blank(),
        legend.direction = "vertical",
        legend.title = element_blank())

Warning: Removed 1257 rows containing non-finite values (stat_pointdensity).

##dy scatter plot with PI

figs2.prtIn %>% 
  filter(rfp.mean.bg.sub.puncta > 0,
         treatment != "none") %>% 
filter(model %in% c("dy.dm","exponential")) %>%
  group_by(cell.id) %>% 
  filter(!(is.na(dy)), 
         ifelse(degron == "5uM", dy > 0.005000000, dy < 0.5)) %>% 
  dplyr::select(cell.id, treatment, dm,dy,aic, model, colony) %>% 
  ungroup() %>% 
  pivot_wider(values_from = c("dy","aic","dm"), names_from = "model")  %>% 
  mutate(dm_dy.dm = ifelse(is.na(dm_dy.dm), Inf, dm_dy.dm),
    dm.level = ifelse(dm_dy.dm > 1, "high", "low")) %>%
  mutate( treatment = case_when(treatment %in% c("dmso1","dmso2") ~ "DMSO",
                                TRUE ~ treatment), 
         treatment = factor(treatment, levels = c("DMSO","1uM","2.5uM","5uM","50uM"))) %>% 
  ggplot(.,aes(y = dy_exponential, x = dy_dy.dm))+
  # geom_hex(bins = 100)+
  ggpointdensity::geom_pointdensity(size = 0.2, adjust = 0.5)+
  geom_abline(slope = 1, color = "red4", alpha = 0.5)+
  facet_wrap(~treatment, scales = "free")+
  theme_pubr()+
  ylab(TeX("Exponential model ($\\delta_{exp}$)"))+
  xlab(TeX("2-parameter model w Maturation ($\\delta_{mat}$)"))+
  labs(title = "Rate of Decay")+
  theme(text = element_text(size = 12),#changed to 12 for GRC from 8
        axis.text.x = element_text(angle = 30), 
        axis.line = element_line(size = 0.1),
        axis.ticks = element_line(size = 0.2),
        strip.background = element_blank(),
        legend.key.size = unit(1.5,"mm"),
        legend.position = c(0.95,0.2),
        legend.background = element_blank(),
        legend.direction = "vertical",
        legend.title = element_blank())

Warning: Removed 1256 rows containing non-finite values (stat_pointdensity).

models with least AIC scores

least.aic.plt <- figS2.df %>% 
  filter(rfp.mean.bg.sub.puncta > 0) %>% 
  filter(model %in% c("dy.dm","exponential")) %>% 
  filter(degron %in% c("mODC","mODC.2","cln2","cln2.3","cln2.2","stable.2","stable"), treatment == "none", red == "pup1-rfp") %>%
  group_by(cell.id) %>% 
  filter(aic == min(aic)) %>% 
  ungroup() %>% 
  # pivot_wider(names_from = name, values_from = value) %>% 
  group_by(degron, colony,model) %>% 
  tally() %>% 
  mutate(perc = (n/sum(n))*100) %>% 
  ungroup() %>% 
  mutate(degron = case_when(degron %in% c("mODC","mODC.2") ~ "GFP-mODC",
                            degron %in% c("cln2.2","cln2","cln2.3") ~ "GFP-CLN2",
                            degron %in% c("stable","stable.2") ~ "GFP"), 
         degron = factor(degron, levels = c("GFP-mODC","GFP-CLN2","GFP"))) %>% 
  mutate(model = case_when(model == "dy.dm" ~ "Eq 6",
                           model == "exponential" ~ "Eq 8")) %>% 
  ggplot(.,aes(x = model, y= perc, color = colony))+
  geom_jitter(width = 0.2, height = 0)+
  stat_summary(fun.y = mean, fun.ymin = mean, fun.ymax = mean,
                 geom = "crossbar", width = 0.5)+
  facet_wrap(~degron, scales = "free_y", ncol = 3)+
  theme_pubr()+
  theme(text = element_text(size = 8), 
        legend.position = "bottom", 
        legend.direction = "horizontal", 
        legend.background = element_blank(), 
        legend.key.size = unit(4 ,"mm"))

least.aic.plt

#patchwork

figs2.final

Warning: Removed 1492 rows containing non-finite values (stat_pointdensity).
Warning: Removed 1491 rows containing non-finite values (stat_pointdensity).

ggsave(plot = figs2.final, path = "~/plots/paper1/figures/fig_1/supplemental_figs/fig_s2/", filename = "aic_dy_unest.pdf", width = 8, height = 6 )

Warning: Removed 1492 rows containing non-finite values (stat_pointdensity).
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ggsave(plot = figs2.final, path = "~/plots/paper1/figures/fig_1/supplemental_figs/fig_s2/", filename = "aic_dy_unest.png", width = 8, height = 6 )

Warning: Removed 1492 rows containing non-finite values (stat_pointdensity).
Warning: Removed 1491 rows containing non-finite values (stat_pointdensity).

#saving individual plots

ggsave(plot = aic.score.ptDenPlt, path = "~/plots/paper1/figures/fig_1/supplemental_figs/fig_s2/GRC/", filename = "aic_scatterPlt.pdf", width = 6, height = 3 )

Warning: Removed 1492 rows containing non-finite values (stat_pointdensity).

#looking at non-exponential decay add the timelapse data

all.exp.list <- read_csv("~/plots/all_data/all_exp_data.csv")

with protein inhibition

all.exp.list %>% 
  filter(treatment != "none") %>% 
  filter(cell.id %in% unique(aic.df$cell.id)) %>% 
  left_join(.,aic.df %>% 
              filter(model == "dy.dm") %>% 
              dplyr::select(cell.id, dm,dy, model,colony), 
            by = "cell.id") %>% 
  group_by(cell.id) %>% 
  mutate(dm.level = ifelse(dm < 1, "low", "high"),
         gfp.int = gfpMeanBgAFsub[1]) %>% 
  na.omit() %>% 
  mutate(It_I0 = gfpMeanBgAFsub/gfpMeanBgAFsub[1]) %>% 
  split(.$treatment) %>% 
  map(.,function(a){
    temp.1 <- a
      # filter(cell.id %in% sample(a$cell.id, 5)) 
    
    slope.df <- a %>% 
      filter(cell.id %in% temp.1$cell.id) %>% 
      mutate(slope1 = (log(It_I0[31]) - 0)/((delta.time[31]/60) - 0) )
    
    temp.1 %>% 
  ggplot(.,aes(x = delta.time/60, y = log(It_I0), group = cell.id, color = cell.id))+
  geom_line(alpha = 0.2)+
  guides(color = "none") +
  geom_abline(data = slope.df , aes(intercept = 0, slope = slope.df$slope1, color = cell.id))+
  facet_wrap(~dm.level, scales = "free_x")+
  theme_pubr()+
      labs(title = a$treatment[1])
  })

are the dm values > dy ?

for experiments without protein inhibition

all.exp.list %>% 
  filter(treatment == "none") %>% 
  filter(cell.id %in% unique(aic.df$cell.id)) %>% 
  left_join(.,aic.df %>% 
              filter(model == "dy.dm") %>% 
              dplyr::select(cell.id, dm,dy, model,colony), 
            by = "cell.id") %>% 
  group_by(cell.id) %>% 
  mutate(dm.level = ifelse(dm < 1, "low", "high"),
         gfp.int = gfpMeanBgAFsub[1]) %>% 
  na.omit() %>% 
  mutate(It_I0 = gfpMeanBgAFsub/gfpMeanBgAFsub[1]) %>% 
  split(.$degron) %>% 
  map(.,function(a){
    temp.1 <- a %>% 
      filter(cell.id %in% sample(a$cell.id, 5)) 
    
    slope.df <- a %>% 
      filter(cell.id %in% temp.1$cell.id) %>% 
      mutate(slope1 = (log(It_I0[31]) - 0)/((delta.time[31]/60) - 0) )
    
    temp.1 %>% 
  ggplot(.,aes(x = delta.time/60, y = log(It_I0), group = cell.id, color = cell.id))+
  geom_line(alpha = 0.2)+
      guides(color = "none")+
  geom_abline(data = slope.df , aes(intercept = 0, slope = slope.df$slope1, color = cell.id))+
  facet_wrap(~dm.level, scales = "free_x")+
  theme_pubr()+
  labs(title = a$degron[1])
  })

$cln2

$cln2.2

$cln2.3

$cln2.4

$mODC

$mODC.2

$mODC.3

$stable

$stable.2

$stable.3

deviation.exp %>% 
  filter(degron %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3")) %>% 
  filter(red == "pup1-rfp") %>% 
  mutate(dm.level = ifelse(dm > 10, "high","low")) %>% 
  ggplot(.,aes(x = delta.y , fill = dm.level))+
  geom_density(aes(y = ..scaled..), alpha = 0.5)+
  facet_wrap(~degron, scales = "free")+
  geom_vline(xintercept = 0)+
  xlim(-0.5,1)

Warning: Removed 68 rows containing non-finite values (stat_density).

deviation.exp %>% 
  filter(red == "pup1-rfp") %>% 
  split(.$degron) %>% 
  map(.,function(a){
    a <- a %>% 
      ungroup() %>% 
      dplyr::select(delta.y, gfp.mean.bg.af.sub.new, rfp.mean.bg.sub.puncta, area, dapi.sum.bg.sub.puncta) %>% 
      na.omit()
    pcor(a)
  })

deviation.exp %>% 
  filter(red == "pup1-rfp") %>% 
  filter(degron %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3")) %>% 
  mutate(degron= case_when(degron == "mODC.2" ~ "yeGFP-mODC", 
                           degron %in% c("cln2.3","cln2.4") ~ "yeGFP-CLN2", 
                           degron %in% c("stable.2" ,"stable.3") ~ "yeGFP"), 
         degron = factor(degron, levels = c("yeGFP-mODC","yeGFP-CLN2","yeGFP"))) %>% 
  ggplot(.,aes(y = delta.y , x = gfp.mean.bg.af.sub.new))+
  ggpointdensity::geom_pointdensity(size = 0.2)+
  geom_smooth(method = "lm")+
  stat_cor()+
  geom_hline(yintercept = 0)+
  facet_wrap(~degron, scales = "free_x")

`geom_smooth()` using formula 'y ~ x'

all.exp.list %>% 
  filter(treatment == "none") %>% 
  filter(cell.id %in% unique(aic.df$cell.id)) %>% 
  left_join(.,aic.df %>% 
              filter(model %in% c("dy.dm", "exponential")) %>% 
              dplyr::select(cell.id, dm,dy, model,colony), 
            by = "cell.id") %>% 
  group_by(cell.id) %>% 
  mutate(dm = ifelse(is.na(dm), Inf, dm),
    dm.level = ifelse(dm < 1, "low", "high"),
         gfp.int = gfpMeanBgAFsub[1]) %>% 
  na.omit() %>% 
  mutate(It_I0 = gfpMeanBgAFsub/gfpMeanBgAFsub[1]) %>% 
  dplyr::select(cell.id, red,model, dy, delta.time,gfpMeanBgAFsub, degron, colony, gfp.int, It_I0, image.no) %>% 
  # filter(image.no == 10) %>% 
  pivot_wider(values_from = dy, names_from = model) %>% 
  mutate(new.It_I0 = -exponential*delta.time/60, 
         ln.It_I0 = log(It_I0), 
         delta.y =  ln.It_I0 - new.It_I0) %>% 
  filter(degron %in% c("mODC.2","cln2.3","cln2.4","stable.2","stable.3")) %>% 
  filter(red == "pup1-rfp") %>% 
  # mutate(dm.level = ifelse(dm > 10, "high","low")) %>% 
  ggplot(.,aes(x = delta.y, y = factor(image.no, levels = c(31:2)) ,fill = degron))+
  stat_density_ridges(geom = "density_ridges_gradient" , 
                      quantiles = 2, 
                      quantile_lines = TRUE , 
                      aes(height = ..ndensity..), 
                      rel_min_height = 0.01, 
                      size = 0.1)+
  geom_vline(xintercept = 0, size = 0.5)+
  xlim(-1,1)

Picking joint bandwidth of 0.0202
Warning: Removed 25592 rows containing non-finite values (stat_density_ridges).

all.exp.list %>% 
  # filter(treatment != "none") %>% 
  filter(cell.id %in% unique(aic.df$cell.id)) %>% 
  left_join(.,aic.df %>% 
              filter(model %in% c("dy.dm", "exponential")) %>% 
              dplyr::select(cell.id, dm,dy, model,colony), 
            by = "cell.id") %>% 
  group_by(cell.id) %>% 
  mutate(dm = ifelse(is.na(dm), Inf, dm),
    dm.level = ifelse(dm < 1, "low", "high"),
         gfp.int = gfpMeanBgAFsub[1]) %>% 
  na.omit() %>% 
  mutate(It_I0 = gfpMeanBgAFsub/gfpMeanBgAFsub[1]) %>% 
  dplyr::select(cell.id,treatment,model, dy, delta.time,gfpMeanBgAFsub, degron, colony, gfp.int, It_I0, image.no) %>% 
  filter(image.no == 15) %>% 
  pivot_wider(values_from = dy, names_from = model) %>% 
  mutate(new.It_I0 = -exponential*delta.time/60, 
         ln.It_I0 = log(It_I0), 
         delta.y =  ln.It_I0 - new.It_I0) %>% 
  dplyr::select(delta.y, cell.id) %>% 
  left_join(.,protInhi.attr, by = "cell.id") %>% 
  filter(treatment != "none") %>% 
  filter(treatment %in% c("dmso1","50uM")) %>% 
  # mutate(dm.level = ifelse(dm > 10, "high","low")) %>% 
  ggplot(.,aes(x = delta.y , fill = treatment))+
  geom_density(aes(y = ..scaled..), alpha = 0.5)+
  geom_vline(xintercept = 0)+
  scale_x_continuous(breaks = seq(-0.5, 0.5, by = 0.1))+
  xlim(-0.5,0.5)

Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
Warning: Removed 116 rows containing non-finite values (stat_density).

all.exp.list %>% 
  # filter(treatment != "none") %>% 
  filter(cell.id %in% unique(aic.df$cell.id)) %>% 
  left_join(.,aic.df %>% 
              filter(model %in% c("dy.dm", "exponential")) %>% 
              dplyr::select(cell.id, dm,dy, model,colony), 
            by = "cell.id") %>% 
  group_by(cell.id) %>% 
  mutate(dm = ifelse(is.na(dm), Inf, dm),
    dm.level = ifelse(dm < 1, "low", "high"),
         gfp.int = gfpMeanBgAFsub[1]) %>% 
  na.omit() %>% 
  mutate(It_I0 = gfpMeanBgAFsub/gfpMeanBgAFsub[1]) %>% 
  dplyr::select(cell.id,treatment,model, dy, delta.time,gfpMeanBgAFsub, degron, colony, gfp.int, It_I0, image.no) %>% 
  # filter(image.no == 15) %>% 
  pivot_wider(values_from = dy, names_from = model) %>% 
  mutate(new.It_I0 = -exponential*delta.time/60, 
         ln.It_I0 = log(It_I0), 
         delta.y =  ln.It_I0 - new.It_I0) %>% 
  dplyr::select(delta.y, cell.id, image.no) %>% 
  left_join(.,protInhi.attr, by = c("cell.id")) %>% 
  filter(treatment != "none") %>% 
  filter(!(treatment %in% c("dmso1","50uM"))) %>% 
  # mutate(dm.level = ifelse(dm > 10, "high","low")) %>% 
 ggplot(.,aes(x = delta.y, y = factor(image.no.x, levels = c(31:2)) ,fill = treatment, alpha = 0.2))+
  stat_density_ridges(geom = "density_ridges_gradient" , 
                      quantiles = 2, 
                      quantile_lines = TRUE , 
                      aes(height = ..ndensity..), 
                      rel_min_height = 0.01, 
                      size = 0.1, 
                      alpha = 0.2)+
  facet_wrap(~treatment, scales = "free")+
  xlim(-1,1)+
  geom_vline(xintercept = 0)

Picking joint bandwidth of 0.0311
Picking joint bandwidth of 0.0236
Picking joint bandwidth of 0.0159
Picking joint bandwidth of 0.0431
Warning: Removed 11680 rows containing non-finite values (stat_density_ridges).